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Comparative Study
. 2021 Aug:228:80-88.
doi: 10.1016/j.ajo.2021.03.058. Epub 2021 May 11.

Classification Criteria for Behçet Disease Uveitis

Standardization of Uveitis Nomenclature (SUN) Working Group
Collaborators
Comparative Study

Classification Criteria for Behçet Disease Uveitis

Standardization of Uveitis Nomenclature (SUN) Working Group. Am J Ophthalmol. 2021 Aug.

Abstract

Purpose: To determine classification criteria for Behçet disease uveitis.

Design: Machine learning of cases with Behçet disease and 5 other panuveitides.

Methods: Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set.

Results: One thousand twelve cases of panuveitides, including 194 cases of Behçet disease with uveitis, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for Behçet disease uveitis were a diagnosis of Behçet disease using the International Study Group for Behçet Disease criteria and a compatible uveitis, including (1) anterior uveitis; (2) anterior chamber and vitreous inflammation; (3) posterior uveitis with retinal vasculitis and/or focal infiltrates; or (4) panuveitis with retinal vasculitis and/or focal infiltrates. The misclassification rates for Behçet disease uveitis were 0.6% in the training set and 0% in the validation set, respectively.

Conclusions: The criteria for Behçet disease uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

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Conflict of interest statement

Conflict of Interest: Douglas A. Jabs: none; Andrew D. Dick: consultant: AbbVie, Alimera, Apitope, Astellas, Gyroscope, Janssen, Roche; James P. Dunn: none; Michal Kramer: none; Neal Oden: none; Peter McCluskey: none; Annabelle A. Okada: consultant: AbbVie Japan, Astellas Pharma Japan, Bayer AG, Daiichi Sankyo; lecture fees: Alcon Pharm Japan, Mitsubishi Tanabe Pharma, Novartis Pharma Japan, Santen Pharmaceutical Corporation, Senju Pharmaceutical Corporation; grant support from Alcon Pharma Japan, Bayer Yakuhin, Mitsubishi Tanabe Pharma; Alan G. Palestine: none; Russell Read: none; Jennifer E. Thorne: Dr. Thorne engaged in part of this research as a consultant and was compensated for the consulting services; Brett E. Trusko: none; Steven Yeh; none.

Figures

Figure 1.
Figure 1.
Fundus photograph of occlusive retinal vasculitis in a patient with Behçet disease
Figure 2.
Figure 2.
Fundus fluorescein angiogram in a patient with Behçet disease, demonstrating retinal non-perfusion and vascular staining due to retinal vasculitis
Figure 3.
Figure 3.
Fundus photograph of focal infiltrates (“white patches”) in a patient with Behçet disease
See this image and copyright information in PMC

Comment in

References

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